The Superintelligence Age and its Economy, or how AI evolves by 2030
Technology brought humanity from the Stone Age to the Agricultural Age to the Industrial Age to the Information Age. Now, the way to the Superintelligence Age is paved with science and engineering, computation and artificial machine intelligence, world knowledge and human intelligence.
The Superintelligence Age could be dominated with Artificial Human Intelligence (as Humanoid AI, AGI or ASI competing Human Intelligence) or Artificial Machine Intelligence Technology (as Real AI or True Machine Intelligence and Learning completing Human Intelligence), with all the implications:
techno-scientific,
social,
economic,
political,
cultural,
religious,
environmental disruptions.
Who first embarks for the Superintelligence Age will lead the world till the end of the world, if any.
The Superintelligence Age
What the world of AI will look like in 2030 is to be defined by a paradigm shift toward Generalized AI Technology as Real Machine Intelligence and Learning (RMIL) converging all narrow, function-specific AI models, ML algorithms, DNNs, Generative AI, Agentic AI techniques, NLPs,, etc.
Superintelligence Technology = Real Machine Intelligence and Learning = the World Modeling AI Machines = Data AI + ML + DL + ANNs + GenAI + LLMs + Agentic AI + Robotics + IoT + Edge AI + Cloud AI +….
By the 1950s, general-purpose, fully programmable digital computers had emerged as the dominant computing architecture combining different physical computers for different special tasks: to calculate the trajectories of missiles, to decipher enemy messages, etc.
Smart phones are integrating the supercomputing and internet access with phone, camera, video recorder, tape recorder, MP3 player, GPS navigator, e-book reader, gaming device, flashlight, compass, etc..
Specialized generative AI tools, as large language models, GPTs, etc., will be disrupted. with all the beneficiaries, the big tech companies led by Nvidia, Apple, Amazon, Microsoft, Alphabet, Meta, Tesla—all investing heavily to obtain their own AI chips produced by the Taiwan Semiconductor Manufacturing Company (TSMC).
Real AI as Machine Superintelligence
OpenAI's CEO, Sam Altman, believes: "it is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there". The Intelligence Age
Scale CEO Alexandr Wang says the Scaling phase of AI has ended and we have entered the Innovating phase where reasoning and other breakthroughs will lead to superintelligence in 6 years or less.
Superintelligence Models: Narrow AI/ML Models > LLMs > World Models > Large World Models > General AI World Models > Man-Machine Hyperintelligence
The Real AI Economy
Thousand and thousand of Generalist AI robots could be designed, developed and deployed in the real AI economy operating in factories, warehouses, hospitals, stores, schools, hotels, homes, etc.
They are to replace humanoid robotics with humanoid robot’s brains, as well as millions of pre-programmed specialized robots in operation around the world today that automate different types of physical activity.
It is reasonable to expect the Real AI Economy, with the following leading industries:,
Aerospace AI Engineering and Technology
Automotive AI Engineering and Technology
Transportation AI Engineering and Technology
Man-Machine AI Engineering and Technology
Internet/Social Media AI Engineering and Technology…
Unlike other conglomerates, holding companies, like Alphabet, we expect the first multi-industry AI companies with related business entities operating in all major future technology industries.
We all could finally see that AI is fundamentally unlike human intelligence, a basic truth that many of us fail to comprehend.
Using human intelligence as the ultimate criteria and benchmark for the development of true MIL fails “to recognize the full range of powerful, profound, unexpected, societally beneficial, utterly non-human abilities that machine intelligence might be capable of”.
With Real AI completing, not competing, human intelligence, the AI-driven job loss will stop to be one of the most widely discussed economic, political and social issues.
By 2030, machine superintelligence will be 1000x more powerful than humans in ways that will completely transform our world.
Transforming Future Business: the Case of Musk's Companies
Elon Musk is publicly known as CEO of Tesla and SpaceX, a revolutionary of space exploration, and the richest one in the world, founding a few companies most people are familiar with, from OpenAI to xAI to "understand the true nature of the universe.”
As such, he owns a constellation of separate businesses, which are reasonable to vertically and horizontally integrate all under xAI, as special divisions of Artificial Intelligence Mega Corporation:
Aerospace AI Engineering and Technology (SpaceX, Starlink, Hyperloop)
Automotive AI Engineering and Technology (Tesla, Robotics)
Transportation AI Engineering and Technology the Boring Co)
Neuro-AI Engineering and Technology (Neuralink Corporation)
Social Media AI Technology (Twitter/X)
Unlike other conglomerates, holding companies, like Alphabet, it could be the world’s first multi-industry AI company with related business entities operating in all major future technology industries.
Resources
"it is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there".
This paper examines the profound challenges that transformative advances in AI towards Artificial General Intelligence (AGI) will pose for economists and economic policymakers. I examine how the Age of AI will revolutionize the basic structure of our economies by diminishing the role of labor, leading to unprecedented productivity gains but raising concerns about job disruption, income distribution, and the value of education and human capital. I explore what roles may remain for labor post-AGI, and which production factors will grow in importance. The paper then identifies eight key challenges for economic policy in the Age of AI: (1) inequality and income distribution, (2) education and skill development, (3) social and political stability, (4) macroeconomic policy, (5) antitrust and market regulation, (6) intellectual property, (7) environmental implications, and (8) global AI governance. It concludes by emphasizing how economists can contribute to a better understanding of these challenges.